Search Results (5)

In this article, we explored how open-source software serves as a strategic enabler for safety and security in the digital transformation of logistics systems. Open source is examined across multiple dimensions, including transparency, community collaboration, digital sovereignty, and long-term infrastructure resilience. The analysis focuses on the logistics domain, where interoperability, critical infrastructure protection, and supply chain continuity are essential. Key elements of open-source development—such as modular architectures, legal and licensing frameworks, and peer-reviewed codebases—support rapid vulnerability management, increased transparency, and the creation of sustainable digital ecosystems. Emphasis is placed on the role of open-source models in strengthening institutional trust, reducing dependency on proprietary vendors, and enhancing responsiveness to cyber threats. Our findings indicate that open source is not merely a technical alternative, but a strategic decision with legal, economic, and political implications, shaping secure, sovereign, and adaptive digital environments—particularly in mission-critical sectors. Full article

Rare-earth-based permanent magnets (PMs) have a vital role in numerous sustainable energy systems, such as electrical machines (EMs). However, their production can greatly harm the environment and their supply chain monopoly presents economic threats. Alternative materials are emerging, but the use of rare-earth PMs remains dominant due to their exceptional performance. Damage to magnet structure can cause loss of performance and efficiency, and propagation of cracks in PMs can result in breaking. In this context, prolonging the service life of PMs and ensuring that they remain damage-free and suitable for re-use is important both for sustainability reasons and cost management. This paper presents a new harmonic content diagnosis and motor performance analysis caused by various magnet structure defects or faults, such as cracked or broken magnets. The proposed method is used for modeling the successive physical failure of the magnet structure in the form of crack formation, crack growth, and magnet breakage. A surface-mounted permanent magnet synchronous motor (PMSM) is studied using simulation in Ansys Maxwell software (Version 2023), and different cracks and PM faults are modeled using the two-dimensional finite element method (FEM). The frequency domain simulation results demonstrate the influence of magnet cracks and their propagation on EM performance measures, such as stator current, distribution of magnetic flux density, back EMF, flux linkage, losses, and efficiency. The results show strong potential for application in health monitoring systems, which could be used to reduce the occurrence of in-service failures, thus reducing the usage of rare-earth magnet materials as well as cost. Full article

With the rapid growth of the software industry, the software supply chain (SSC) has become the most intricate system in the complete software life cycle, and the security threat situation is becoming increasingly severe. For the description of the SSC, the relevant research mainly focuses on the perspective of developers, lacking a comprehensive understanding of the SSC. This paper proposes a chain portrait framework of the SSC based on a resource perspective, which comprehensively depicts the threat model and threat surface indicator system of the SSC. The portrait model includes an SSC threat model and an SSC threat indicator matrix. The threat model has 3 levels and 32 dimensions and is based on a generative artificial intelligence model. The threat indicator matrix is constructed using the Attack Net model comprising 14-dimensional attack strategies and 113-dimensional attack techniques. The proposed portrait model’s effectiveness is verified through existing SSC security events, domain experts, and event visualization based on security analysis models. Full article

An electric vehicle charging station (EVCS) infrastructure is the backbone of transportation electrification; however, the EVCS has various vulnerabilities in software, hardware, supply chain, and incumbent legacy technologies such as network, communication, and control. These standalone or networked EVCSs open up large attack surfaces for local or state-funded adversaries. The state-of-the-art approaches are not agile and intelligent enough to defend against and mitigate advanced persistent threats (APT). We propose data-driven model-free digital clones based on multiple independent agents deep reinforcement learning (IADRL) that uses the Twin Delayed Deep Deterministic Policy Gradient (TD3) to efficiently learn the control policy to mitigate the cyberattacks on the controllers of EVCS. Also, the proposed digital clones trained with TD3 are compared against the benchmark Deep Deterministic Policy Gradient (DDPG) agent. The attack model considers the APT designed to malfunction the duty cycles of the EVCS controllers with Type-I low-frequency attacks and Type-II constant attacks. The proposed model restores the EVCS operation under threat incidence in any/all controllers by correcting the control signals generated by the legacy controllers. Our experiments verify the superior control policies and actions of TD3-based clones compared to the DDPG-based clones. Also, the TD3-based controller clones solve the problem of incremental bias, suboptimal policy, and hyperparameter sensitivity of the benchmark DDPG-based digital clones, enforcing the efficient mitigation of the impact of cyberattacks on EVCS controllers. Full article

Federated learning has emerged as a promising technique for the Internet of Things (IoT) in various domains, including supply chain management. It enables IoT devices to collaboratively learn without exposing their raw data, ensuring data privacy. However, federated learning faces the threats of local data tampering and upload process attacks. This paper proposes an innovative framework that leverages Trusted Execution Environment (TEE) and blockchain technology to address the data security and privacy challenges in federated learning for IoT supply chain management. Our framework achieves the security of local data computation and the tampering resistance of data update uploads using TEE and the blockchain. We adopt Intel Software Guard Extensions (SGXs) as the specific implementation of TEE, which can guarantee the secure execution of local models on SGX-enabled processors. We also use consortium blockchain technology to build a verification network and consensus mechanism, ensuring the security and tamper resistance of the data upload and aggregation process. Finally, each cluster can obtain the aggregated parameters from the blockchain. To evaluate the performance of our proposed framework, we conducted several experiments with different numbers of participants and different datasets and validated the effectiveness of our scheme. We tested the final global model obtained from federated training on a test dataset and found that increasing both the number of iterations and the number of participants improves its accuracy. For instance, it reaches 94% accuracy with one participant and five iterations and 98.5% accuracy with ten participants and thirty iterations. Full article